{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T23:40:49Z","timestamp":1772667649779,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011975","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T00:00:00Z","timestamp":1715126400000}}],"reference-count":55,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"DOE","award":["FP00007607"],"award-info":[{"award-number":["FP00007607"]}]},{"name":"NIH","award":["R01NS118648"],"award-info":[{"award-number":["R01NS118648"]}]},{"DOI":"10.13039\/100007000","name":"Laboratory Directed Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007000","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: <jats:italic>\u03bc<\/jats:italic>ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011975","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T17:37:42Z","timestamp":1714153062000},"page":"e1011975","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model"],"prefix":"10.1371","volume":"20","author":[{"given":"Rui","family":"Meng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1974-4603","authenticated-orcid":true,"given":"Kristofer E.","family":"Bouchard","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"pcbi.1011975.ref001","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1146\/annurev-neuro-092619-094115","article-title":"Computation through neural population dynamics","volume":"43","author":"S Vyas","year":"2020","journal-title":"Annual Review of Neuroscience"},{"key":"pcbi.1011975.ref002","first-page":"1881","article-title":"Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity","author":"MY Byron","year":"2009","journal-title":"Advances in neural information processing systems"},{"issue":"7405","key":"pcbi.1011975.ref003","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1038\/nature11129","article-title":"Neural population dynamics during reaching","volume":"487","author":"MM Churchland","year":"2012","journal-title":"Nature"},{"issue":"7441","key":"pcbi.1011975.ref004","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1038\/nature11911","article-title":"Functional organization of human sensorimotor cortex for speech articulation","volume":"495","author":"KE Bouchard","year":"2013","journal-title":"Nature"},{"issue":"38","key":"pcbi.1011975.ref005","doi-asserted-by":"crossref","first-page":"12662","DOI":"10.1523\/JNEUROSCI.1219-14.2014","article-title":"Control of spoken vowel acoustics and the influence of phonetic context in human speech sensorimotor cortex","volume":"34","author":"KE Bouchard","year":"2014","journal-title":"Journal of Neuroscience"},{"key":"pcbi.1011975.ref006","article-title":"Not optimal, just noisy: the geometry of correlated variability leads to highly suboptimal sensory coding","author":"JA Livezey","year":"2022","journal-title":"bioRxiv"},{"key":"pcbi.1011975.ref007","article-title":"Gaussian process based nonlinear latent structure discovery in multivariate spike train data","volume":"30","author":"A Wu","year":"2017","journal-title":"Advances in neural information processing systems"},{"issue":"10","key":"pcbi.1011975.ref008","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1038\/s41592-018-0109-9","article-title":"Inferring single-trial neural population dynamics using sequential auto-encoders","volume":"15","author":"C Pandarinath","year":"2018","journal-title":"Nature methods"},{"key":"pcbi.1011975.ref009","first-page":"454","article-title":"Neural dynamics discovery via gaussian process recurrent neural networks. 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